2 Review of the literature
4.1 Creative thinking and FL proficiency
Based on the arguments outlined in §3.3, SEM was used with the lavaan pack-age for R (Rosseel 2012) to allow for a) investigation of latent constructs and b) consideration of measurement error to increase model accuracy.
For the first research question, FL proficiency (L2 French and L3 English) was introduced to the model as an endogenous (or outcome) variable. Based on the assumption of an underlying aptitude for foreign language learning, the two lan-guages were treated as one latent variable. The choice is further based on the multicompetence framework which hypothesizes the integration of all languages known to the learner in a common linguistic repertoire (for a recent discussion
see Cook 2012). Creative thinking (TCT-DP) and intelligence (number sequenc-ing CFT-20 R) were introduced to the model as exogenous (or predictor) vari-ables.
In Figure 1, circles show latent variables (or constructs) and squares show mea-sured variables. One headed arrows indicate the regression coefficients or pa-rameter estimates, i.e. the strength of association between the variables. Double headed arrows stand for variances.
Figure 1: Path diagram for Model 3 (reliability 0.78). Nodes (latent constructs): fs = foreign language proficiency, c = creative thinking, iq = intelligence; TCT_stand = test on divergent thinking, ZF_stand = number sequencing test, FrCT_stand = French C-Test, EST_stand = English OYLPT score.
In order to determine measurement error, the reliability coefficients for the exogenous (predictor) variables (creative thinking, intelligence) were taken from
the test manuals to calculate error variances (Table 1). The obtained error vari-ances were added to the different statistical Models 1–3.
For the number sequencing test, a reliability coefficient of Cronbach’s𝛼0.91 is reported (Weiß 2006). For the TCT-DP, different values are given, ranging from 0.38 to 0.78, depending on the validation study (Urban & Jellen 1995). Based on recommendations by Westfall & Yarkoni (2016), I accounted for the TCT-DP vari-ation by fitting three models assuming reliability coefficients of 0.38 (Model 1), 0.58 (Model 2) and 0.78 (Model 3). For ease of reading, only Model 3 will be reported in the following. Further information on all models can be found at https://osf.io/vxr9m/.
Table 1: Exogenous (predictor) variables and their sample variance (Var), standard deviation (SD), reported reliability coefficients (RC) and calculated measurement error/error variance (ME) for this sample.
Model 3 Var SD RC ME
Creative thinking (divergent thinking) 0.01 0.12 0.78 0.003 Intelligence (number sequencing) 0.06 0.25 0.91 0.006 Model 3 produced acceptable fit indices2(CFI= 1,RMSEA= 0,SRMR= 0.01).
As shown in Table 2 and Figure 1, Model 3 supports an effect for creative think-ing on language proficiency when intelligence is controlled for in the range of statistical significance. This result does not necessarily point to a direct causal influence from creative thinking to FL proficiency. The design used in this study does not allow for making claims on causality.
Table 2: Estimates for Model 3 (0.78) on the association between cre-ative thinking and intelligence and FL proficiency.
L2/L3 proficiency ~ Est. SE 𝑧 𝑝 ci lower ci upper Creative thinking 0.47 0.19 2.47 0.013 0.1 0.84
Intelligence 0.25 0.08 3.1 0.002 0.09 0.41
4.2 Creative thinking and FL motivation
The opportunity to be creative in the classroom is assumed to impact particu-larly on intrinsic FL motivation, rather than extrinsic motivation. The SEM for
2Cut off for good fit: CFI> 0.9;RMSEA< 0.08,SRMR< 0.08(Kline 2011).
the second research question therefore includes intrinsic motivation for English and French as the endogenous (outcome) variable and creative thinking as the exogenous (predictor) variable. Different models with combinations of motiva-tion items were fitted. A total of four items were retained in the final Model 4 represented in Figure 2 (the same symbols apply as in Figure 1). Combinations of other FL motivation items fitted the data less well and were therefore not pursued further.3
Figure 2: Path diagram for Model 4 (reliability 0.78). Nodes:
c = creative thinking, mot = intrinsic motivation (latent con-structs); TCT1 = test on divergent thinking, intrinsic motivation French: QFr_T1_FB01, QFr_T2_FB06, intrinsic motivation English:
QEng_T2_FB25_QEng_T2_FB06.
3Kolenikov & Bollen (2012) describe several possible causes for unusual model indications, re-ferred to as Heywood cases: Outliers, empirical underidentification, structural misspecification, missing data or sampling fluctuations. The authors also give an overview of how to address these issues.
Again, different measurement errors were considered for the creative think-ing test in Model 4, all options yielded the same acceptable fit to the data (CFI= 1,SRMR = 0.04,RMSR = 0). The association between creative thinking and in-trinsic motivation turns out to be negligible and non-significant, as indicated by the low𝑧-value and high𝑝-value reported in Table 3.
Table 3: Estimates from Model 4 (0.78) on the association between mo-tivation to learn a foreign language and creative thinking.
Intrinsic Motivation ~ Est. SE 𝑧 𝑝 ci lower ci upper Creative thinking 0.36 0.74 0.49 0.63 −1.1 1.81
5 Discussion
Research question 1 addressed the association between creative (divergent) think-ing and FL proficiency. A statistically significant effect emerges from the data, indicating that creative thinking plays a role in children’s developing FL pro-ficiency when they are taught in the TBLT paradigm. The present study thus mirrors findings from Ottó (1998) with high school students and contradicts Al-bert (2006) and AlAl-bert & Kormos (2011) who could not replicate results from the Ottó study. Research question 2 explored the possibility that creative children are more motivated to learn foreign languages with TBLT than their peers, i.e.
that high scores in the creative thinking test are associated with high values on the motivation-questionnaire items. This hypothesis could not be substantiated:
the association between creative thinking and FL motivation in this sample is negligible and non-significant.
It is worth pointing out that the results reported here do not allow for stipu-lating any causal links between the investigated constructs. While an effect of creative thinking on FL proficiency has been found in the data, the direction of causality remains unclear. It may well be, as some scholars suggest, that language learning contributes to creative thinking. Such claims have been made mainly with reference to simultaneous bilingualism (for an overview, see Ricciardelli 1992), rather than exposure to instructed language learning. To address causal-ity, Simonton (2008) suggests a longitudinal design with multiple assessment of the variables where time would allow for comparison within and between sub-jects. If multilingualism resulting from instructed language learning were a pre-dictor for creative thinking, this would be detected in the data if an individual’s
FL proficiency at T1 and creative thinking at T2 were more strongly correlated than creative thinking at T1 and individual FL proficiency at T2 (Simonton 2008:
154). However, this kind of research is rare and obviously the research design presented in this chapter does not allow for such inferences.
Some changes to the design could have improved the robustness of the find-ings. For instance, children’s motivation to learn foreign languages did not in-clude questions on how they liked the teaching methods or textbooks. Also, as-sessing creative thinking more comprehensively, including a range of tests and information on creative hobbies might have provided a more detailed view of the creative student than a mere non-verbal test. These aspects may be considered in future studies to provide further insights into the role of creative thinking in instructed language learning.
References
Albert, Ágnes. 2006. Learner creativity as a potentially important individual vari-able: Examining the relationships between learner creativity, language apti-tude and level of proficiency. University of Pécs Roundtable 2006: Empirical Studies in English Applied Linguistics. 77. http://mek.niif.hu/04900/04902/
04902.pdf#page=72 (15 April, 2017).
Albert, Ágnes & Judit Kormos. 2011. Creativity and narrative task performance:
An exploratory study.Language Learning61(s1). 73–99.
Arnet-Clark, Illya, Silvia Frank Schmid, Linda Grimes, Guido Ritter, Jean Rüdiger-Harper, Sandra Thum-Widmer & Chantal Villiger. 2013.New World: English as a second foreign language. Baar: Klett und Balmer.
Bertschy, Ida, Barbara Grossenbacher, Esther Sauer & Andi Thommen. 2012.Mille feuilles. Bern: Schulverlag plus AG.
Brunner, Jerry & Peter C. Austin. 2009. Inflation of Type I error rate in multiple regression when independent variables are measured with error.The Canadian Journal of Statistics/La Revue Canadienne de Statistique 37(1). 33–46. https://
www.jstor.org/stable/25653459 (18 April, 2019).
Carroll, John B. 1964. The prediction of success in intensive foreign language training. InTraining research and education, 87–136. Pittsburgh: University of Pittsburgh Press.
Carroll, John B. 1993. Abilities in the domain of idea production. InHuman cog-nitive abilities: A survey of factor analytic studies, 394–439. Cambridge: Cam-bridge University Press.
Cook, Vivian J. 2012. Multicompetence. In Carol A. Chapelle (ed.),The encyclope-dia of applied linguistics, 1–6. Hoboken, NJ, USA: Blackwell Publishing.DOI:
10.1002/9781405198431.wbeal0778.
Cropley, Arthur. 2006. In praise of convergent thinking.Creativity Research Jour-nal18(3). 391–404.
Deci, Edward L. & Richard M. Ryan. 1985. Conceptualizations of intrinsic moti-vation and self-determination. In Edward L. Deci & Richard M. Ryan (eds.), Intrinsic motivation and self-determination in human behavior, 11–40. Boston:
Springer.DOI: 10.1007/978-1-4899-2271-7_2.
Deci, Edward L. & Richard M. Ryan (eds.). 2002.Handbook of self-determination research. Rochester, NY: University of Rochester Press.
Dörnyei, Zoltán & Stephen Ryan. 2015.The psychology of the language learner revisited. New York: Routledge.
Ellis, Rod. 2009. Task-based language teaching: Sorting out the misunderstand-ings.International Journal of Applied Linguistics 19(3). 221–246.DOI: 10.1111/j.
1473-4192.2009.00231.x.
Ellis, Rod. 2017. Task-based language teaching. In Shawn Loewen & Masatoshi Sato (eds.),The Routledge handbook of instructed second language acquisition, 108–125. New York: Routledge.
Finke, Ronald A., Thomas B. Ward & Steven M. Smith. 1992.Creative cognition.
Massachusetts: MIT Press.
Fleith, Denise de Souza, Joseph S. Renzulli & Karen L. Westberg. 2002. Effects of a creativity training program on divergent thinking abilities and self-concept in monolingual and bilingual classrooms.Creativity Research Journal 14(3-4).
373–386.
Gardner, Robert C. 1990. Attitudes, motivation, and personality as predictors of success in foreign language learning. In Thomas S. Parry & Charles W. Stans-field (eds.),Language aptitude reconsidered. Washington, D.C.: ERIC Clearing-house.
Ghonsooly, Behzad & Sara Showqi. 2012. The effects of foreign language learning on creativity.English Language Teaching5(4). 161.DOI: 10.5539/elt.v5n4p161.
Grigorenko, Elena L., Robert J. Sternberg & Madeline E. Ehrman. 2000. A theory-based approach to the measurement of foreign language learning ability: The Canal-F theory and test.The Modern Language Journal84(3). 390–405.
Grotjahn, Rüdiger. 2002. Konstruktion und Einsatz von C-Tests: Ein Leitfaden für die Praxis. In Rüdiger Grotjahn (ed.),Der C-Test. Theoretische Grundlagen und praktische Anwendung, vol. 4, 211–255. Bochum: AKS-Verlag.
Guilford, Joy Paul. 1959. Three faces of intellect.American Psychologist 14. 469–
479.
Kharkhurin, Anatoliy V. 2012. Multilingualism and creativity. Bristol: Multilin-gual Matters.
Kline, Rex B. 2011.Principles and practice of structural equation modeling. 3rd edn.
New York: The Guilford Press.
Kolenikov, Stanislav & Kenneth A. Bollen. 2012. Testing negative error variances:
Is a Heywood case a symptom of misspecification?Sociological Methods & Re-search41(1). 124–167.
Krashen, Stephen. 1981.Second language acquisition and second language learning.
Oxford: Pergamon Press. http://sdkrashen.com/content/books/sl_acquisition_
and_learning.pdf (6 July, 2018).
Lubart, Todd I. 1994. Creativity. In Robert Sternberg (ed.), Handbook of percep-tion and cognipercep-tion. Thinking and problem solving, 290–323. Cambridge, Mas-sachusetts: Academic Press.
Ottó, István. 1998. The relationship between individual differences in learner cre-ativity and language learning success.TESOL quarterly 32(4). 763–773. http:
//www.jstor.org/stable/3588011 (12 May, 2017).
Oxford English Testing. 2013.Oxford young learners placement test. https://www.
oxfordenglishtesting.com/ (5 May, 2017).
Ricciardelli, Lina A. 1992. Creativity and bilingualism. The Journal of Creative Behavior26(4). 242–254.DOI: 10.1002/j.2162-6057.1992.tb01183.x.
Rohrer, Julia M. 2018. Thinking clearly about correlations and causation: Graph-ical causal models for observational data.Association for Psychological Science 1(1). 27–42.DOI: 10.1177/251524591774562.
Rosseel, Yves. 2012. Lavaan: An R package for structural equation modeling. Jour-nal of Statistical Software48(2). 1–36.
Runco, Mark A. 2004. Creativity.Annual Review of Psychology55. 657–687. http:
//www.annualreviews.org/toc/psych/55/1 (12 May, 2017).
Showqi, Sara & Behzad Ghonsooly. 2015. The Nature of Metacognitive Awareness in Foreign Language Learners and Its Interaction with Creativity.Journal of Language Teaching and Research6.
Simonton, Dean K. 2008. Bilingualism and creativity. InAn introduction to bilin-gualism principles and processes, 147–166. Hillsdale, NJ: Lawrence Erlbaum As-sociates.
Skehan, Peter. 1996. A framework for the implementation of task-based instruc-tion.Applied Linguistics17(1). 38–62.DOI: 10.1093/applin/17.1.38.
Sternberg, Robert J. 1985.Beyond IQ: A triarchic theory of human intelligence. Cam-bridge: CUP Archive.
Sternberg, Robert J. & Linda A. O’Hara. 1999. Creativity and intelligence. In Robert J. Sternberg (ed.),Handbook of Creativity, 251–272. Cambridge: Cam-bridge University Press.
Urban, Klaus K. & Hans G. Jellen. 1995.Test zum Schöpferischen Denken – Zeich-nerisch. Frankfurt am Main: Pearson PsychCorp.
Vanhove, Jan. 2015.Controlling for confounding variables in correlational research:
Four caveats. https://janhove.github.io/design/2015/08/24/caveats-confounds-correlational-designs (28 April, 2020).
Vogt, Thomas. 2010. Kreativität und Kognition. InKalkulierte Kreativität, 189–231.
VS Verlag für Sozialwissenschaften.DOI: 10.1007/978-3-531-92340-6_9.
Weiß, Rudolf H. 2006.Manual Grundintelligenztest Skala 2 – Revision (CFT 20-R).
Göttingen: Hogrefe.
Westfall, Jacob & Tal Yarkoni. 2016. Statistically controlling for confounding con-structs is harder than you think.PLOS One 11(3).DOI: 10.1371/journal.pone.
0152719.
Willis, Jane. 1996. A flexible framework for task-based learning. In David Willis
& Jane Willis (eds.),Challenge and change in language teaching, 52–62. New York: Macmillan Education.